An integrated MRP and finite scheduling system to derive detailed daily schedules for a manufacturing shop

Many companies rely on Material Requirements Planning (MRP) to support their Production Scheduling and Control (PS&C) functions. Since MRP does not provide a detailed shop floor schedule, these users have to implement either a third party procedure or an internally developed procedure for shop floor controls. In this thesis we consider a class of user shops which are characterized by the following features: Homogenous machines, that is all machines can produce all products. Each product requires a setup, but several products may have a common setup. MRP requirements are specified on a weekly basis while actual requirements are specified on a hourly basis. Specifically, we develop a MRP and Finite Scheduling System (MFSS) which calculates the weekly net change requirements of products, then generates the detailed daily job order schedules, and finally sequences jobs on machine queues. The objectives of the system are to maximize the utilization of the machines and to minimize setup times. The MFSS was programmed on a personal computer-based system utilizing off-the-shelf relational database software

The Integration of Process Planning and Shop Floor Scheduling in Small Batch Part Manufacturing

In this paper we explore possibilities to cut manufacturing leadtimes and to improve delivery performance in a small batch part manufacturing shop by integrating process planning and shop floor scheduling. Using a set of initial process plans (one for each order in the shop), we exploit a resource decomposition procedure to determine schedules to determine schedules which minimize the maximum lateness, given these process plans. If the resulting schedule is still unsatisfactory, a critical path analysis is performed to select jobs as candidates for alternative process plans. In this way, an excellent due date performance can be achieved, with a minimum of process planning and scheduling effort

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

A Petri net simulation model for virtual construction of earthmoving operations

A common and extended Petri net simulation framework for virtual construction of earthmoving operations is developed to simulate dynamic changes of workflow and information flow in the earthmoving construction process and illustrate the constraint relationship between various operational equipment and construction restrictions. The proposed framework considers factors that influence earthmoving operations including randomness of construction activities, individual preference of equipment scheduling, and constraint relationship between equipment and construction environment. With the given equipment availability and project indirect cost, the framework can predict construction situation, equipment utilization rate, estimated duration and cost to achieve visualized and intelligent scheduling of virtual construction process in earthmoving operations. The simulation process is conducted on the CPNTools platform. The data required by the research were collected on-site in an actual case. The randomness of construction activities in earthmoving operations and main factors influencing construction are simulated. The sensitivity analysis for the model is carried out. The study will provide technical support and a management basis for equipment scheduling of earthmoving operations